AbstractA continuous contour dataset at 100 m intervals for all land south of 60°S, excluding the Balleny Islands. The vertical datum of the contours is EGM2008. Contours are extracted primarily from the PGC Reference Elevation Model of Antarctica (REMA) v1.1 with certain islands filled from Copernicus WorldDEM. Further small areas are interpreted from satellite imagery, and Peter I Øy contours are from the Norwegian Polar Institute. Sources of individual line segments are contained in the attribute table and full compilation information is given in the lineage statement.Note: contours overlap the coastline in small areas, due to resolution of the data used in creation of the lines, and potential errors in coastline and/or contour data. Certain areas are known to contain erroneous data due to faults in the original DEM data.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related medium resolution dataset at 500 m intervals is also published via Living Atlas.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageAll processing described here was performed in ArcGIS Pro version 2.6.A composite Digital Elevation Model (DEM) was created comprising of three datasets from the Reference Elevation Model of Antarctica v1.1: ‘REMA_100m_peninsula_dem_filled’, ‘REMA_100m_dem’ and ‘REMA_200m_dem_filled’. These DEMs were first converted from ellipsoidal height to height above EGM2008 geoid and then mosaicked together in respective order at 100 m spatial resolution. This 100 m DEM was smoothed by performing ‘Focal Statistics’ using a 3x3 cell size.100 m contours were extracted and all contours with a height <1m were deleted, as well as erroneous offshore contours. All contour ‘dangles’ were identified and then fixed to create a continuous dataset. They were fixed either by interpreting the correct line from satellite imagery or from ‘Copernicus WorldDEM 90m’ contours. Such lines are attributed with ‘interpreted’ in the source field and should be treated with caution. In other locations where the contours significantly overlapped the coastline, contours were redrawn/interpreted to not go offshore. In certain locations, primarily some islands on the Antarctic Peninsula, REMA data was insufficient to produce contours. In these places, contours were produced from the ‘Copernicus WorldDEM 90m’ DEM and smoothed by 300 m using a PAEK smoothing algorithm. Contours for Peter I Øy were incorporated from the Norwegian Polar Institute Data at 100 m intervals. The source of every line is written in the attribute table.All contours were merged together and lines <150 m in length were deleted. Further lines <1500 m were deleted in ‘non-mountainous’ regions, so as to avoid deleting small mountain peak contours but to still simplify the main dataset. These regions were interpreted manually using the hillshade of the DEM used to produce the contours.Original DEM sources and citations:REMA: Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019, 2019.Copernicus WorldDEM: produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.Norwegian Polar Institute (2014). Map data / kartdata Peter I Øy 1:50 000 (P50 Kartdata). Norwegian Polar Institute. https://doi.org/10.21334/npolar.2014.29105abcCitationPlease cite this item as: 'Gerrish, L., Fretwell, P., & Cooper, P. (2020). High resolution vector contours for Antarctica (7.3) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/4bd20a2b-df7d-46a2-acdf-5104c82ff4c7'If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, accessed [year]'

This layer uses sand, silt, and clay most likely values from soilgrids.org to create texture classes. Soilgrids.org sand, silt, and clay datasets are integers that give a weight in grams in each particle class. The weight we are converting directly into percent, for example soilgrids value of 500g of sand means 50% sand ((500g/1kg) * 100 = 50%).A 100cm depth was chosen because it matches many of the world's most important crops' rooting depths. A 0 to 60cm version of this is also available.Variable mapped: Predominant USDA texture class as derived from predicted percent sand, silt, and clay.Data Projection: Goode's Homolosine (land) WKID 54052Mosaic Projection: Goode's Homolosine (land) WKID 54052Extent: World, except AntarcticaCell Size: 250 mSource Type: ThematicVisible Scale: All scales are visibleSource: SoilGrids.orgPublication Date: June 14, 2021NOTE: This layer uses the USDA texture classification system with international soil datasets, which use different particle size definitions than the USDA. Very little silt shows up in this layer, this could be a reason why.To determine the predominant soil texture we first classified texture for the following layer depths:0-5cm5-15cm15-30cm30-60cm60-100cmThen we used focal statistics with the majority option to find the majority texture class of each pixel from the five layers, weighted as follows:0-5cm * 15-15cm * 215-30cm * 330-60cm * 660-100cm * 7 (not 8, something had to break the tie and I reduced the multiplier by 1 to break ties, thinking of all soil depths the depth from 95-100cm may be the least significant in the stack overall.)-----------------------------------------------------------------Raster functions were created to classify sand, silt, and clay using the following statements in raster calculator:Sand Con((( Silt + ( 1.5 * Clay )) < 150 ), 1, 0)Loamy Sand Con(((Silt + (1.5 * Clay)) >= 150) & ((Silt + (2 * Clay)) < 300),2, 0)Sandy Loam Con(((Clay

=70)&(Clay<200)&(Sand>520)&((Silt + (2 * Clay)) = 300))|((Clay<70)&(Silt<500)&((Silt + (2 * Clay)) = 300)),4, 0)Loam Con(((Clay>=70) & (Clay<270) & (Silt>=280) & (Silt<500) & (Sand<=520)),8 ,0)Silt LoamCon((((Silt>=500) & (Clay>=120) & (Clay<270)) | ((Silt>=500) & (Silt<800) & (Clay<120))),16 , 0)SiltCon(((Silt >= 800)&(Clay<120)),32 ,0)Sandy Clay LoamCon(((Clay>=200) & (Clay < 350) & (Silt < 280) & (Sand > 450)),64 ,0)Clay LoamCon(((Clay >= 270) & (Clay<400) & (Sand > 200) & (Sand <= 450)), 128, 0)Silty Clay LoamCon(((Clay >= 270) & (Clay < 400) & (Sand <= 200)),256 ,0)Sandy ClayCon(((Clay >= 350) & (Sand > 450)) ,512 , 0)Silty Clay Con(((Clay >= 400) & (Silt >= 400)), 1024, 0)Clay Con(((Clay>=400) & (Sand <= 450) & (Silt < 400)) , 2048 , 0 )These conditionals were used on the "mean" soilgrids.org rasters for silt, sand, and clay on rasters representing the following depths:0-5 cm below the land surface5-15cm below the land surface15-30cm below the land surface30-60cm below the land surface60-100cm below the land surfaceThe conditionals were just summed together to create check rasters for each depth. All analysis was done in soilgrids.org own Goode's Homolosine projection (land) in ArcGIS Pro. The data were served in this same projection in ArcGIS Image for ArcGIS Online.---------------------------------------------------------------------------------------------------At first, the classes were given a value of 1, 2, 4, 8, 16, 32 and so on, then were added together. This is so we could see if some classes were overlapping others. We continued to troubleshoot the above definitions until there were no overlaps and as few values of 0 as possible. Once the overlaps and misses were fixed, the dataset was reclassed into values of 1-13. An attribute table was built to drive popups and a legend.